The present invention relates generally to the field of physical activity monitoring, and more specifically to apparatus and methods of monitoring and measuring posture and physical activity using acceleration data for research, training, monitoring, sports, leisure, therapeutic and rehabilitation purposes.
Uni-axial and tri-axial accelerometers have been used for many years in wearable devices that estimate physical activity. The tri-axial devices provide acceleration data for three orthogonal axes, plus the vector magnitude.
Many conventional systems integrate the accelerometer data over a time window (an “activity count”), and use linear or higher-order formulas or look-up tables to estimate Energy Expenditure (EE), expressed in units such as METS (multiple of basal metabolic rate), or calories. The offset and scaling factors of the conversion equations are often determined by statistical curve-fitting, using population-sample accelerometer data collected concurrently with reference EE measurements, such as from oxygen consumption or doubly-labeled water.
The applicant has recognized that existing systems have shortcomings, including one or more of the following:
1) Existing systems do not monitor both the intensity of the physical activity of the user and his/her posture.
2) Since the accelerometer senses the combination of the effects of gravity and acceleration, either a high-pass accelerometer sensor (such as some piezoelectric sensors) or a high-pass filter after a wideband DC sensor (such as a suspended proof-mass MEMS accelerometer) is used to suppress the gravity signal. However, this filtering method does not accurately suppress the gravity signal for the following two reasons: (a) the wearer's posture can change during the measurement, creating an AC component to the sensed gravity signal that can feed through the high-pass filter, and (b) for many daily-living situations, the gravity signal is much stronger than the acceleration signal, and it is difficult to accurately filter out a strong signal from a weak one.
3) Existing systems that use a low-pass filter to suppress the acceleration signal perform the filtering function on both the direction and magnitude of the raw signal. This leads to inaccuracies in the resulting calculation of acceleration, as the filter can allow feed-through of either component of the raw signal in either or both directions.
4) Existing systems which subtract a calibrated value of gravity from the total signal to determine the magnitude of acceleration do not correct the calculation for differences in direction between gravity and the user's acceleration, causing errors in the resulting value of acceleration.
5) Accelerometer sensor accuracy cannot be monitored or adjusted in the field without periodic calibration that requires user manipulation and interrupts data capture.
6) Existing systems require precise attachment, aligned with the wearer's vertical, anterior-posterior, or medio-lateral axes, and do not correct for alignment errors between the sensor axes and those of the wearer, or for changes in the alignment during the measurement process, or for changes in the wearer's posture during the measurement process.
7) Existing systems do not have a means to automatically detect the influence of modes of conveyance such as elevators, automobiles, or airplanes on measured acceleration.
8) Existing systems correlate step counts or total dynamic acceleration (either uni-axial, tri-axial vector, or tri-axial sum-of-axes) to Energy Expenditure (EE), and do not discriminate between physical activity related to changes in potential energy (EEP, i.e. displacement against gravity), and physical activity related to changes in kinetic energy (EEK, i.e. acceleration against inertia). Since the relationships between acceleration and these two types of energy are different, this leads to measurement errors. In addition, separate measures of these two types of physical activity can be useful for purposes of monitoring, analysis, and feedback to the user.
9) Existing systems assume a fixed ratio between vertical and horizontal Energy Expenditure.
10) Existing systems analyze the frequency or pattern of acceleration to estimate activity levels. However, pattern analysis is prone to misinterpretation, especially in daily-living situations where movements can be complex and unpredictable.
11) Existing systems use multiple accelerometers in different orientations, and determine which accelerometer is most favorably aligned with the wearer's motion during a sampling interval. However, there are many situations where no single accelerometer is exactly aligned.
12) Existing systems use other positional sensors in addition to a single multi-axis linear accelerometer, such as rotational (gyroscopes or additional accelerometers), magnetic, or locational sensors (GPS or altimeter), or physiological sensors, such as heart-rate, muscular activity, ventilation (breathing), or skin-temperature sensors, to improve the measurement of posture or activity. However, the addition of these other sensors increases the cost and complexity of the system, and makes it difficult to incorporate the system as software into a device such as a cell phone or wearable music player, which already incorporates a single multi-axis accelerometer but not these other sensor types.
It would therefore be desirable to have improved apparatus and methods of monitoring and measuring posture and physical activity using multi-axis accelerometer sensors that would avoid the drawbacks of the above-described conventional systems.